So only 61.9% is suitable for resistance analysis! We can now filter +
So only 61.1% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -644,7 +644,7 @@ on it with the data_1st <- data %>% filter_first_isolate()
data_1st <- data %>% filter_first_isolate()
So we end up with 12 380 isolates for analysis. Now our data looks +
So we end up with 12 221 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,380 -Available: 12,380 (100%, NA: 0 = 0%) +Length: 12,221 +Available: 12,221 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5808562
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1169,19 +1169,19 @@ own: Hospital A -0.5816024 +0.5930677 Hospital B -0.5940042 +0.5801668 Hospital C -0.5499199 +0.6017699 Hospital D -0.5802964 +0.5680230 @@ -1206,23 +1206,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5816024 -3707 +0.5930677 +3664 Hospital B -0.5940042 -4303 +0.5801668 +4316 Hospital C -0.5499199 -1873 +0.6017699 +1808 Hospital D -0.5802964 -2497 +0.5680230 +2433 @@ -1247,27 +1247,27 @@ therapies very easily: Escherichia -0.6685241 -0.6546775 -0.8850051 +0.6648247 +0.6451390 +0.8791228 Klebsiella -0.6527571 -0.6766021 -0.8971684 +0.6555387 +0.6676783 +0.8945372 Staphylococcus -0.6686082 -0.6548130 -0.8868792 +0.6771822 +0.6474732 +0.8885145 Streptococcus -0.4778857 +0.4683270 0.0000000 -0.4778857 +0.4683270 @@ -1295,23 +1295,23 @@ classes, use a antibiotic class selector such as Hospital A -58.2% -35.3% +59.3% +35.6% Hospital B -59.4% -37.5% +58.0% +36.7% Hospital C -55.0% -34.5% +60.2% +37.5% Hospital D -58.0% -36.5% +56.8% +35.5% @@ -1427,16 +1427,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 64 0.002 2 16 8 128 0.025 0.0625 0.001 -# [11] 0.0625 0.005 16 128 4 0.025 0.01 128 0.001 16 -# [21] 128 0.025 4 0.002 0.01 64 4 0.125 >=256 128 -# [31] 1 0.002 0.005 64 0.25 0.025 16 32 32 1 -# [41] 8 2 32 16 0.0625 16 8 >=256 >=256 0.25 -# [51] 64 0.25 128 16 0.0625 0.001 4 0.0625 0.005 0.5 -# [61] 64 0.0625 0.025 0.001 0.001 0.005 0.0625 0.025 0.25 1 -# [71] 0.125 0.002 8 0.125 0.001 0.001 0.01 16 8 32 -# [81] 0.002 0.01 1 0.01 1 0.0625 0.025 0.01 1 1 -# [91] 0.025 0.125 8 4 32 >=256 4 0.001 4 0.005
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 64 0.002 2 16 8 128 0.025 0.0625 0.001 -# [11] 0.0625 0.005 16 128 4 0.025 0.01 128 0.001 16 -# [21] 128 0.025 4 0.002 0.01 64 4 0.125 >=256 128 -# [31] 1 0.002 0.005 64 0.25 0.025 16 32 32 1 -# [41] 8 2 32 16 0.0625 16 8 >=256 >=256 0.25 -# [51] 64 0.25 128 16 0.0625 0.001 4 0.0625 0.005 0.5 -# [61] 64 0.0625 0.025 0.001 0.001 0.005 0.0625 0.025 0.25 1 -# [71] 0.125 0.002 8 0.125 0.001 0.001 0.01 16 8 32 -# [81] 0.002 0.01 1 0.01 1 0.0625 0.025 0.01 1 1 -# [91] 0.025 0.125 8 4 32 >=256 4 0.001 4 0.005
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 26 25 22 31 23 25 21 17 28 29 23 26 21 19 29 21 25 27 25 28 28 30 23 26 17 -# [26] 29 27 25 28 21 26 28 24 27 31 22 21 23 18 29 26 30 30 28 18 25 20 30 29 17 -# [51] 18 21 27 24 29 21 24 22 26 27 18 25 28 31 19 18 18 28 19 22 18 27 28 31 21 -# [76] 31 18 17 27 20 26 24 17 26 23 19 20 18 25 26 26 24 24 29 30 19 19 23 31 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")